Evrim stratejisi

Bilişim Biliminde; Evrim Stratejileri (ES), evrim ve adaptasyon fikri optimizasyon tekniğine dayanmaktadır. Bu evrimsel hesaplama veya yapay evrim metodolojilerinin genel sınıfına aittir.

Tarih
Evrim strateji optimizasyon tekniği 1960'larda oluşturuldu ve İngo Rechenberg ve Hans-Paul Schwefel birlikte çalışmalarıyla 1970'lerde geliştirildi.

Metotlar
Evrim strateji, operatör araştırmalarına göre doğal bağımlı temsilinde, öncelikli mutasyon ve seleksiyonda kullanılmaktadır. Evrimsel algoritmaları, operatörlerin döngü uygulamalarıdır. Bir döngünün yinelenmesinde jenerasyonu çağırıyor. Jenerasyonların sırası kritiklerin sonu gelinceye kadar devam eder.

Evrim stratejilerin çağdaş türevleri sık sık μ parametresinin nüfusta, fazladan operatörlerin birleşmesiyle kullanılmaktadır, (μ/ρ+, λ)-ES çağırmaktadır.

Kaynakça
Bibliyografi
Ingo Rechenberg (1971): Evolutionsstrategie – Optimierung technischer Systeme nach Prinzipien der biologischen Evolution (PhD thesis). Reprinted by Fromman-Holzboog (1973).
Hans-Paul Schwefel (1974): Numerische Optimierung von Computer-Modellen (PhD thesis). Reprinted by Birkhäuser (1977).
H.-G. Beyer and H.-P. Schwefel. Evolution Strategies: A Comprehensive Introduction. Journal Natural Computing, 1(1):3–52, 2002.
Hans-Georg Beyer: The Theory of Evolution Strategies: Springer April 27, 2001.
Hans-Paul Schwefel: Evolution and Optimum Seeking: New York: Wiley & Sons 1995.
Ingo Rechenberg: Evolutionsstrategie '94. Stuttgart: Frommann-Holzboog 1994.
J. Klockgether and H. P. Schwefel (1970). Two-Phase Nozzle And Hollow Core Jet Experiments. AEG-Forschungsinstitut. MDH Staustrahlrohr Project Group. Berlin, Federal Republic of Germany. Proceedings of the 11th Symposium on Engineering Aspects of Magneto-Hydrodynamics, Caltech, Pasadena, Cal., 24.–26.3. 1970.

Araştırma merkezleri
Bionics & Evolutiontechnique at the Technical University Berlin
Chair of Algorithm Engineering (Ls11) – University of Dortmund
Collaborative Research Center 531 – University of Dortmund

Dış bağlantılar
:A peer-reviewed discussion of the subject.
Animation: Optimization of a Two-Phase Flashing Nozzle with an Evolution Strategy. Animation of the Classical Experimental Optimization of a two phase flashing nozzle made by Professor Hans-Paul Schwefel and J. Klockgether. The result was shown at the Proceedings of the 11th Symposium on Engineering Aspects of Magneto-Hydrodynamics, Caltech, Pasadena, Cal., 24.–26.3. 1970.
CMA Evolution Strategy – a contemporary variant where the complete covariance matrix of the multivariate normal mutation distribution is adapted.
Comparison of Evolutionary Algorithms on a Benchmark Function Set – The 2005 IEEE Congress on Evolutionary Computation: Session on Real-Parameter Optimization - The CMA-ES (Covariance Matrix Adaptation Evolution Strategy) applied in a benchmark function set and compared to nine other Evolutionary Algorithms.
Evolution Strategies – A brief description.
Evolution Strategies Animations - Some interesting animations and real world problems (such as format of lenses, bridges configurations, etc) solved through Evolution Strategies.
Evolution Strategy in Action – 10 ES-Demonstrations. By Michael Herdy and Gianino Patone – 10 problems solved through Evolution Strategies.
Evolutionary Algorithms Demos – There are some applets with Evolution Strategies and Genetic Algorithms that the user can manipulate to solve problems. Very interesting for a comparison between the two Evolutionary Algorithms.
Evolutionary Car Racing Videos – The application of Evolution Strategies to evolve cars' behaviours.
EvoWeb. – The European Network of Excellence in Evolutionary Computing.
Learning To Fly: Evolving Helicopter Flight Through Simulated Evolution – A (10 + 23)-ES applied to evolve a helicopter flight controller.
Professor Hans-Paul Schwefel talks to EvoNews – An interview with Professor Hans-Paul Schwefel, one of the Evolution Strategy pioneers.